A novel approach to motion tracking with wearable sensors based on Probabilistic Graphical Models

Wearable motion tracking systems represent a breakthrough in ecological motion tracking. Their effectiveness has been proved in many fields, from performance assessment to human-robot interaction. Most of the approaches are based on the exploitation of optimal probabilistic filtering of inertial motion units (IMUs) signals, ranging from linear Kalman Filters (KF) to Particle filters (PF). Since most of the models are highly nonlinear, filters such as Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are typically used. These approaches cause all the variables of the models to be correlated each other. Probabilistic Graphical Models (PGM) are a framework for probabilistic reasoning that allows to explicitly declare the actual dependencies among variables. In this paper we propose a novel algorithm for motion tracking with IMUs based on PGM. The model is compared to the state of the art UKF algorithm in tracking the human upper limb. The results show that the proposed approach perform a slightly better compared to the UKF.